Proxy Individual Positioning via IEEE 802.11 Monitor Mode and Fine-Tuned Analytics

Indoor positioning of individuals is one of the most important technologies in smart home applications for user-customized support. The indoor positioning is typically fulfilled through radio signal strength indicators (RSSIs) of referred devices with specific media such as Wi-Fi access points (APs)...

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Veröffentlicht in:IEEE Vehicular Technology Conference S. 1 - 5
Hauptverfasser: You, Myungsung, Park, Soochang, Lee, Sang-Hoon, Yang, Taehun
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2019
Schlagworte:
IEEE 802.11 Standard
Mobile handsets
Monitoring
Probes
Servers
Support vector machines
Wireless fidelity
ISSN:2577-2465
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Zusammenfassung:Indoor positioning of individuals is one of the most important technologies in smart home applications for user-customized support. The indoor positioning is typically fulfilled through radio signal strength indicators (RSSIs) of referred devices with specific media such as Wi-Fi access points (APs) and Wi-Fi station devices (STAs). So, the capability of typical positioning schemes is highly close to the signal acquisition environments of such media. In other words, since STAs frequently fall into a sleep mode to save the battery power and some data acquisition technologies are based on advertising intervals, the amount of RSSIs of referring devices could not be gathered enough to detect correct positions of individuals. In this paper, a novel individual positioning mechanism, called PIPing (Proxy Individual Positioning), is come up with. The PIPing mechanism carries out proxy signal acquisition via IEEE 802.11 monitor mode devices to overcome such restrictions. In addition, PIPing includes machine learning based signal data analytics to provide high reliable results for positioning. Based on the proof-of-concept prototype, PIPing can acquire much higher amount of RSSI data than existing manners, about 330% increment; the reliability of positioning for a home with seven rooms shows 96.4% via the support vector machine (SVM) and 96.5% by the multilayer perceptron (MLP) with autoencoder denoising to tune up signals.
ISSN:2577-2465
DOI:10.1109/VTCFall.2019.8891177